{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2547970904737342"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X, y = make_blobs(centers=2, random_state=4, n_samples=30)  #make_blobs 生成多类单标签数据集，默认含两个特征\n",
    "\n",
    "data = np.random.normal(loc=5,scale=1,size=10) #loc均值,scale标准差,数量\n",
    "data.shape\n",
    "\n",
    "np.std(data)/np.mean(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.374540\n",
       "1    0.950714\n",
       "2    0.731994\n",
       "3    0.598658\n",
       "4    0.156019\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "pd.Series(rng.rand(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193\n",
       "New York    141297  19651127\n",
       "Florida     170312  19552860\n",
       "Illinois    149995  12882135"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = pd.Series({'California': 423967, 'Texas': 695662,\n",
    "                  'New York': 141297, 'Florida': 170312,\n",
    "                  'Illinois': 149995})\n",
    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,\n",
    "                 'New York': 19651127, 'Florida': 19552860,\n",
    "                 'Illinois': 12882135})\n",
    "data = pd.DataFrame({'area':area, 'pop':pop})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  423967, 38332521],\n",
       "       [  695662, 26448193],\n",
       "       [  141297, 19651127],\n",
       "       [  170312, 19552860],\n",
       "       [  149995, 12882135]], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "      <td>114.806121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop     density\n",
       "California  423967  38332521   90.413926\n",
       "Texas       695662  26448193   38.018740\n",
       "New York    141297  19651127  139.076746\n",
       "Florida     170312  19552860  114.806121\n",
       "Illinois    149995  12882135   85.883763"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['density'] = data['pop'] / data['area']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop\n",
       "Florida   170312  19552860\n",
       "Illinois  149995  12882135"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['Florida':'Illinois','area':'pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', '4'], dtype='<U11')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals1 = np.array([1, '2', 3, 4])\n",
    "vals1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<U11')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "fig, ax = plt.subplots(1,2)\n",
    "\n",
    "# Call plot() method on the appropriate object\n",
    "ax[0].plot(x, np.sin(x))\n",
    "ax[1].plot(x, np.cos(x));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
